{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Capillary Pressure JFunc.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOFg6o8Hvlx88JCDCaHlAVj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/Capillary_Pressure_JFunc.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IhFp_uQFQWT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qTqaSA_pFZVO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sigma = 50 #IFT, dyn/cm\n",
        "\n",
        "phi_lab = 0.16\n",
        "k_lab = 80\n",
        "\n",
        "phi = 0.19 #porosity\n",
        "\n",
        "k = 120 #md\n",
        "\n",
        "pc_lab = {'Sw':[1.0,0.8,0.6,0.4,0.25,0.23,0.21,0.20],\n",
        "      'Pc_lab':[0.5,0.6,0.75,1.05,1.50,1.75,3.0,8.0]}\n",
        "\n",
        "df = pd.DataFrame(pc_lab)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KNwBn8AyGrLl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "6118aa21-3ddd-4906-f16f-054b2c583a64"
      },
      "source": [
        "df"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sw</th>\n",
              "      <th>Pc_lab</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.80</td>\n",
              "      <td>0.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.40</td>\n",
              "      <td>1.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.25</td>\n",
              "      <td>1.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.21</td>\n",
              "      <td>3.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.20</td>\n",
              "      <td>8.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Sw  Pc_lab\n",
              "0  1.00    0.50\n",
              "1  0.80    0.60\n",
              "2  0.60    0.75\n",
              "3  0.40    1.05\n",
              "4  0.25    1.50\n",
              "5  0.23    1.75\n",
              "6  0.21    3.00\n",
              "7  0.20    8.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imdNVpLcFx4j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "bca05c08-2196-4796-8d81-e7154c14d609"
      },
      "source": [
        "plt.style.use('default')\n",
        "\n",
        "plt.figure(figsize=(8,4))\n",
        "\n",
        "plt.plot(df['Sw'],df['Pc_lab'],label='Pc v/s Sw',color='red',linewidth=3)\n",
        "\n",
        "plt.title('Laboratory Data')\n",
        "plt.xlabel('Sw')\n",
        "plt.ylabel('Pc')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.grid()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbINfr9RIQbg",
        "colab_type": "text"
      },
      "source": [
        "Now our task is to convert this lab data to reservoir data. \n",
        "\n",
        "Concept: J function."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fDQRdWjIbnx",
        "colab_type": "text"
      },
      "source": [
        "> $J(Sw) = 0.21645 \\frac{Pc}{\\sigma} root(k/phi) $"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbWw-XvoF26I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['J'] = 0.21645*(df['Pc_lab']/sigma)*(np.sqrt(k_lab/phi_lab))"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEajfbTZJM5C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "d77db40d-6b8c-448a-bd44-17323a089d46"
      },
      "source": [
        "df"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sw</th>\n",
              "      <th>Pc_lab</th>\n",
              "      <th>J</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.048400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.80</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.058080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.072600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.40</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.101639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.25</td>\n",
              "      <td>1.50</td>\n",
              "      <td>0.145199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.75</td>\n",
              "      <td>0.169399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.21</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.290398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.20</td>\n",
              "      <td>8.00</td>\n",
              "      <td>0.774395</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Sw  Pc_lab         J\n",
              "0  1.00    0.50  0.048400\n",
              "1  0.80    0.60  0.058080\n",
              "2  0.60    0.75  0.072600\n",
              "3  0.40    1.05  0.101639\n",
              "4  0.25    1.50  0.145199\n",
              "5  0.23    1.75  0.169399\n",
              "6  0.21    3.00  0.290398\n",
              "7  0.20    8.00  0.774395"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GdYan_6SJr5V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['Pc_reservoir'] = (df['J']*sigma)/(0.21645*np.sqrt(k/phi))"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ei-LJBPKhqP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "f38b56a7-2dbb-434e-9428-3e81a3eeea87"
      },
      "source": [
        "df"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sw</th>\n",
              "      <th>Pc_lab</th>\n",
              "      <th>J</th>\n",
              "      <th>Pc_reservoir</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.048400</td>\n",
              "      <td>0.444878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.80</td>\n",
              "      <td>0.60</td>\n",
              "      <td>0.058080</td>\n",
              "      <td>0.533854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.60</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.072600</td>\n",
              "      <td>0.667317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.40</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.101639</td>\n",
              "      <td>0.934244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.25</td>\n",
              "      <td>1.50</td>\n",
              "      <td>0.145199</td>\n",
              "      <td>1.334635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.75</td>\n",
              "      <td>0.169399</td>\n",
              "      <td>1.557074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.21</td>\n",
              "      <td>3.00</td>\n",
              "      <td>0.290398</td>\n",
              "      <td>2.669270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.20</td>\n",
              "      <td>8.00</td>\n",
              "      <td>0.774395</td>\n",
              "      <td>7.118052</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Sw  Pc_lab         J  Pc_reservoir\n",
              "0  1.00    0.50  0.048400      0.444878\n",
              "1  0.80    0.60  0.058080      0.533854\n",
              "2  0.60    0.75  0.072600      0.667317\n",
              "3  0.40    1.05  0.101639      0.934244\n",
              "4  0.25    1.50  0.145199      1.334635\n",
              "5  0.23    1.75  0.169399      1.557074\n",
              "6  0.21    3.00  0.290398      2.669270\n",
              "7  0.20    8.00  0.774395      7.118052"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XrtihhaUKik1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "b984e2a8-d923-4382-a2f3-d0371b25ede1"
      },
      "source": [
        "plt.style.use('default')\n",
        "\n",
        "plt.figure(figsize=(12,6))\n",
        "\n",
        "plt.plot(df['Sw'],df['Pc_lab'],label='Pc(Lab) v/s Sw',linewidth=2)\n",
        "plt.plot(df['Sw'],df['Pc_reservoir'],label='Pc(Actual) v/s Sw',linewidth=2)\n",
        "\n",
        "plt.title('Application of Leverett-J function')\n",
        "plt.xlabel('Sw(Fraction)')\n",
        "plt.ylabel('Pc(Psi)')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.grid()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eeUVms1kdOnS44Do7d+6skJAQTZ06tdA+zfkjbfkjcQVH3gzD0IsvvljofPnhtahwVJCvr686d+6sxYsXu0yB/9///qeUlBTdcMMNCg0NveD3UZwaNWqoefPmevfdd11q2rp1q5YvX66bb775kq9RnDvuuENff/21Pv/880LPHTt2THl5ec7HPj4+6tOnj5YsWaL33ntPeXl5LtPJ88934MAB/fvf/y50vqysLJ08efKc9fj6+qp3795auHBhkUGz4BZr5+rHoKCgIm8j6Nixo8vP2SPel+LKK6/U8ePHtXnzZuexgwcPatGiRRd1Ph8fH/Xq1UtLlizRd999V+j5/O/6hX6fJenmm2/Wn3/+6XIvfl5enl5++WUFBwc7t50DALgHI9wA4MVSU1OVkZGhW2+9tcjnr7/+ekVGRio5OdklWNWrV0833HCDhg8frpycHM2aNUtVq1YtNC05ICBAn332mQYOHKiWLVtq2bJl+vTTT/XYY48Ve49vUUJDQzVz5kzdd999iouLU0JCgsLDw7Vp0yZlZmbq3XffVYMGDXTllVdq3LhxOnDggEJDQ7Vw4cIi7zW99tprJUmjR49Wly5d5Ovr69zW6mzPPPOMVqxYoRtuuEEjRoyQn5+f3nzzTeXk5GjatGkX/B7OZ/r06erWrZtatWqlIUOGOLcFq1KlSqF9wUtq4cKFzhHMggYOHKjx48crNTVVt9xyiwYNGqRrr71WJ0+e1JYtW7RgwQLt3r1bERERztf069dPL7/8siZPnqwmTZoU2n7q7rvv1rx58/TAAw9o1apVatOmjex2u3755RfNmzdPn3/+eZELgBX0z3/+U6tWrVLLli11//33q1GjRjpy5Ih++OEHrVy5UkeOHJF0KuCGhYXpjTfeUEhIiIKCgtSyZUvVqVNH1157rT788EM9/PDDiouLU3BwsHr06HFJn+P53HnnnZo4caJuu+02jR49WpmZmXr99ddVv379i17o7bnnntPy5csVHx/v3Gbt4MGDmj9/vtauXauwsDA1b95cvr6+ev7553X8+HH5+/vrpptuUrVq1Qqdb+jQoXrzzTc1aNAgff/994qJidGCBQu0bt06zZo1SyEhIZf6MQAASsJDq6MDANygR48eRkBAgHHy5Mli2wwaNMgwm83G33//7bId0gsvvGBER0cb/v7+Rtu2bY1Nmza5vG7gwIFGUFCQsXPnTqNz585GYGCgcfnllxuTJ0827Ha7S1udZ1uwfKmpqUbr1q0Nq9VqhIaGGtddd53xwQcfOJ/ftm2b0bFjRyM4ONiIiIgw7r//fudWSwW3jsrLyzMefPBBIzIy0jCZTC5bNp1di2EYxg8//GB06dLFCA4ONgIDA4327dsb69evd2lT3JZS+VtzFbVl09lWrlxptGnTxvn+evToYWzbtq3I85VkW7DifvK37srIyDAmTZpk1KtXz7BYLEZERITRunVr41//+peRm5vrck6Hw2FER0cbkoxnnnmmyOvm5uYazz//vBEbG2v4+/sb4eHhxrXXXmskJSUZx48fd7aTZIwcObLIc/zvf/8zRo4caURHRxtms9moXr260aFDB+Ott95yabd48WKjUaNGhp+fn0s/nzhxwkhISDDCwsIMSRe0Rdg777xjSDJ++OGHc7YrblswwzCM5cuXG40bNzYsFotx9dVXG++//36x24IV9d5r165tDBw40OXYnj17jHvuuceIjIw0/P39jbp16xojR440cnJynG3+/e9/G3Xr1jV8fX1dvm9nbwtmGKc+23vvvdeIiIgwLBaL0aRJk0Jbq53rPRb1bwQAcHFMhsGqGACAU3bv3q06depo+vTpGjdu3DnbDho0SAsWLNCJEyfcVB1waV566SU99NBD+u2331y26wIAoKxwDzcAAKgUNmzYoKCgIBYOAwC4DfdwAwAAr7Zw4UKlpaUpOTlZ9913n/z8+L8/AAD34H9xAACAVxs3bpwyMjI0ZMgQzZw509PlAAAqEe7hBgAAAACgDHAPNwAAAAAAZYDADQAAAABAGajQ93A7HA798ccfCgkJkclk8nQ5AAAAAAAvZxiGMjIyFBUVJR+fc49hV+jA/ccffyg6OtrTZQAAAAAAKpl9+/apZs2a52xToQN3SEiIpFNvNDQ01MPVFM9ms2n58uXq3LmzzGazp8tBMeinioF+Kv/oo4qBfqoY6KeKgX4q/+ijiqGi9FN6erqio6OdefRcKnTgzp9GHhoaWu4Dd2BgoEJDQ8v1F6eyo58qBvqp/KOPKgb6qWKgnyoG+qn8o48qhorWTxdyWzOLpgEAAAAAUAYI3AAAAAAAlAECNwAAAAAAZaBC38MNAAAAoOIyDEN5eXmy2+1leh2bzSY/Pz9lZ2eX+bVw8cpLP/n6+srPz69Utp4mcAMAAABwu9zcXB08eFCZmZllfi3DMFS9enXt27evVEIUykZ56qfAwEDVqFFDFovlks5D4AYAAADgVg6HQ7t27ZKvr6+ioqJksVjKNGA5HA6dOHFCwcHB8vHhrtryqjz0k2EYys3N1V9//aVdu3bpqquuuqRaCNwAAAAA3Co3N1cOh0PR0dEKDAws8+s5HA7l5uYqICCAwF2OlZd+slqtMpvN2rNnj7Oei8W3DQAAAIBHEH5RXpXWd5NvOAAAAAAAZYDADQAAAABAGSBwAwAAAICbHD58WNWqVdPu3btL7ZwxMTGaNWtWsc/n5uYqJiZG3333XaldExeGwA0AAAAAF2DQoEEymUwymUyyWCyqV6+epkyZory8vAs+x7PPPquePXsqJiZGkrR7926ZTCZt3LixjKqWLBaLxo0bp4kTJ5baOevUqaOVK1eW+HW7du1SQkKCoqKiFBAQoJo1a6pnz5765ZdfSq228oTADQAAAAAXqGvXrjp48KB27NihRx55RImJiZo+ffoFvTYzM1Nvv/22hgwZUsZVFjZgwACtXbtWP/300yWfa/PmzTp69Kji4+NL9DqbzaZOnTrp+PHj+uijj/Trr7/qww8/VJMmTXTs2LFLrqs8InADAAAAwAXy9/dX9erVVbt2bQ0fPlwdO3ZUamqq8/l169apXbt2CgwMVHh4uLp06aKjR49KkpYuXSp/f39df/31F3y9nTt3qmfPnrr88ssVHBysuLi4IkeWMzIy1L9/fwUFBemKK67Qq6++6vJ8eHi42rRpo7lz5xZ5HYfDoZo1a+r11193Of7jjz/Kx8dHe/bscR5bvHixunbt6tw6q0ePHgoPD1dQUJBiY2O1dOnSIq/x008/aefOnXrttdd0/fXXq3bt2mrTpo2eeeYZ52cycOBAPfjgg87XjBkzRiaTyTkCnpubq6CgoIsaXfcEj+7DbbfblZiYqPfff19//vmnoqKiNGjQID3xxBNluvE9AAAAgPKlx8tr9VdGThmd3ZDDMORjMklyzRmRIf5a8uANF31mq9Wqw4cPS5I2btyoDh06aPDgwXrxxRfl5+enVatWyW63S5LWrFmja6+9tkTnP3HihG6++WY9++yz8vf313/+8x/16NFDv/76q2rVquVsN336dD322GNKSkrS559/roceekj169dXp06dnG2uu+46rVmzpsjr+Pj4qH///kpJSdHw4cOdx5OTk9WmTRvVrl3beSw1NVUPP/ywJGnkyJHKzc3VV199paCgIG3btk3BwcFFXiMyMlI+Pj5asGCBxowZI19f30Jt2rRpo//85z/Ox6tXr1ZERITS0tLUoEEDbdiwQTabTa1bt77AT9CzPBq4n3/+eb3++ut69913FRsbq++++0733nuvqlSpotGjR3uyNAAAAABu9FdGjv5Mz/Z0GRfMMAx98cUX+vzzz50jstOmTVOLFi302muvOdvFxsY6f9+zZ4+ioqJKdJ1mzZqpWbNmzsdPP/20Fi1apNTUVI0aNcp5vE2bNnr00UclSfXr19e6des0c+ZMl8AdFRXlMlJ9tgEDBuiFF17Q3r17VatWLTkcDs2dO1dPPPGEs82BAwe0efNmdevWTZK0d+9e9e7dW02aNJEk1a1bt9jzX3HFFXrppZc0YcIEJSUlqUWLFmrfvr0GDBjgfF3++/jrr7/k5+enbdu26cknn1RaWpoeeOABpaWlKS4uToGBgSX5GD3Go4F7/fr16tmzp7p37y7p1Op6H3zwgb799ltPllWqDMPQuAVbtHufjzb7/KonezT2dEkAAABAuRMZ4l+GZz/3CHdJfPLJJwoODpbNZpPD4VBCQoISExMlnRrh7tu3b7GvzcrKUkBAQImud+LECSUmJurTTz/VwYMHlZeXp6ysLO3du9elXatWrQo9PnvlcqvVqszMzGKv1bx5czVs2FApKSl69NFHtXr1ah06dMjlPaWmpuqGG25QWFiYJGn06NEaPny4li9fro4dO6p3795q2rRpsdcYOXKk7rnnHqWlpembb77R/Pnz9dxzzyk1NVUdOnRQo0aNdNlll2n16tWyWCy65pprdMsttzinyK9evVrt2rW7oM+uPPBo4G7durXeeustbd++XfXr19emTZu0du1azZgxo8j2OTk5ysk5M80kPT1d0qmb7202m1tqvhifbvlTeQ4f5e48XK7rrOzy+4Y+Kt/op/KPPqoY6KeKgX6qGOinkrPZbDIMQw6HQw6HQ5K0eGTZTRE2DEMZGRkKCQkp8tbV/Bou5Dzt2rXTa6+9JovFoqioKPn5+TnPYbVane+rKFWrVtWRI0dcns//veBnUdAjjzyilStXatq0aapXr56sVqvuuOMO5eTkuLQ/+7qGYRR6b4cPH1ZkZOQ5329CQoJSUlI0YcIEJScnq0uXLgoPDz/TT4sXq0ePHs7HgwcPVqdOnfTpp59qxYoVmjp1qv71r3+5jL6fLSgoSN27d1f37t01ZcoUde3aVc8884xuuukmmUwmtW3bVqtWrZK/v7/i4+PVuHFj5eTkaPPmzVq/fr0efvjhC+6zi+VwOGQYhmw2W6Gp7yX5t+7RwP3oo48qPT1dDRo0kK+vr+x2u5599lkNGDCgyPZTp05VUlJSoePLly8v11MKzCZf5cmkw8cyil1AAOXHihUrPF0CLgD9VP7RRxUD/VQx0E8VA/104fz8/FS9enWdOHFCubm5brtuRkbGJb3eZrPJ399f1apVk6RCo8UNGjTQ8uXLnfc3n61hw4aaN2+ec+BQOjWCLUknT550OZ5vzZo1uvPOO9WhQwdn+127dqlVq1bO9g6HQ2vXrnV5/Zo1a1SvXj2XYz/++KNiY2OLvE6+Hj166Mknn9RXX32lBQsWaMaMGc72J06cUFpamqZNm+ZyjipVqighIUEJCQlKSkrSm2++qXvuuafYa5ytbt26+vbbb53907JlS7377rvy9/fXE088oRMnTqhVq1aaOnWqcnJy1KRJk3O+h9KQm5urrKwsffXVV4W2fTvXLIGzeTRwz5s3T8nJyUpJSVFsbKw2btyoMWPGKCoqSgMHDizUftKkSS5f3vT0dEVHR6tz584KDQ11Z+kl8syWNGWdyJWPJUA331yypfPhPjabTStWrFCnTp1kNps9XQ6KQT+Vf/RRxUA/VQz0U8VAP5Vcdna29u3bp+Dg4BJPsb4Y5xvhvlBms1l+fn7FZo8nn3xSzZo106RJkzRs2DBZLBatWrVKffv2VUREhG699VZNmTJFdrtd4eHhkuRcYGz//v0KCgpyOV9sbKyuvvpqLV26VL1795bJZNJTTz0lwzBksVicdfj4+Ojbb7/Vm2++qZ49e2rlypVavHixlixZ4lLrf//7XyUlJZ0zOzVu3FitW7fWmDFj5HA41K9fP1mtVkmnBjrr16+vxo3P3CY7duxYde3aVfXr19fRo0f19ddfKzY2tshrbNy4UYmJibrrrrvUqFEjWSwWrV69WsnJyZowYYJCQkKUkZGhzp0767HHHpPFYlHnzp0VHBysjh07avz48YqLi1ONGjUupLsuSXZ2tqxWq2688cZC39GShH2PBu7x48fr0Ucf1Z133ilJatKkifbs2aOpU6cWGbj9/f3l71/4Hguz2Vyu/8stwHxqCkJOnqNc14lTyvv3CafQT+UffVQx0E8VA/1UMdBPF85ut8tkMsnHx0c+PmW/U3H+9OP8a14sk5c4OtcAACAASURBVMl0znPkj3A/9thjuv7662W1WtWyZUsNGDBAPj4+atasmf7xj39owYIFGjZsmCQ5z5WQkFDofPv27dPMmTM1ePBg3XDDDYqIiNDEiROVkZFRqI5HHnlE33//vaZMmaLQ0FDNmDHDubCZJH399dc6fvy47rjjjvN+BgMGDNCIESN0zz33uPwRYMmSJbr11ltdXu9wOPTggw9q//79Cg0NVdeuXTVz5swir1GrVi3VqVNHTz/9tHbv3i2TyaSYmBglJSVp7NixznZNmzZVWFiY6tev7wzu7du3l91uV7t27dzynfHx8ZHJZCry33VJ/p17NHBnZmYW+rB8fX3LfD6+u1lPB+5sm93DlQAAAAC4WHPmzDlvm/j4eK1bt67Y55966imNHz9e999/v3x8fBQTE+O837o4X375pcvjkSNHujzevXv3eeuaNWuWxo8f7xytPpfhw4e7bA0mSXl5eVq6dKmWLVvmcvzll18+7/nyRURE6MUXXyz2+fwc6OPjoyNHjrg817x58/N+TuWRRwN3jx499Oyzz6pWrVqKjY3Vjz/+qBkzZmjw4MGeLKvUBVhO/VEhy3bqxnv2GAcAAAAqp+7du2vHjh06cOCAoqOj3XLN3NxcNWnSxGUUuaSOHDmisWPHKi4urhQr834eDdwvv/yynnzySY0YMUKHDh1SVFSUhg0bpqeeesqTZZW6/BFu6dS08gBz4Q3eAQAAAFQOY8aMcev1LBaLy17aF6NatWqXfI7KyKOBOyQkRLNmzSq0P5y3CfA7E7Czcu0EbgAAAACoBMr+bnMowHzmY87O4z5uAAAAAKgMCNxuUHBKeVYugRsAAAAAKgMCtxsEWAoEblYqBwAAAIBKgcDtBgF+BaaUE7gBAAAAoFIgcLtBwSnl2Tbv2mMcAAAAAFA0ArcbBHAPNwAAAABUOgRuNyi4Sjn3cAMAAACV1+HDh1WtWjXt3r3b06VckMTERDVv3tz5+NFHH9WDDz7owYoqFgK3GwS4TCkncAMAAAAV0aBBg2QymWQymWSxWFSvXj1NmTJFeXl5F3yOZ599Vj179lRMTEyh57p06SJfX19t2LChRHXNmTNHYWFhJXrNxRo3bpzeffdd/f7776VyvqSkJN11110lfp3dbtc///lPNWjQQFarVZdddplatmyp//u//yuVukoLgdsNrARuAAAAwCt07dpVBw8e1I4dO/TII48oMTFR06dPv6DXZmZm6u2339aQIUMKPbd3716tX79eo0aN0jvvvFPaZZeaiIgIdenSRa+//nqpnG/x4sW69dZbS/y6pKQkzZw5U08//bS2bdumVatWaejQoTp27Fip1FVaCNxuwJRyAAAAwDv4+/urevXqql27toYPH66OHTsqNTXV+fy6devUrl07BQYGKjw8XF26dNHRo0clSUuXLpW/v7+uv/76QuedPXu2brnlFg0fPlwffPCBsrKyXJ4/duyYhg0bpssvv1wBAQFq3LixPvnkE6Wlpenee+/V8ePHnaPviYmJkiSTyaSPP/7Y5TxhYWGaM2eO8/HEiRNVv359BQYGqm7dunryySdls9nO+Rn06NFDc+fOLfb5xx57TC1btix0vFmzZpoyZYrz8b59+/TTTz+pa9euMgxDSUlJaty4saxWq6KiojR69Ohir5GamqoRI0aob9++qlOnjpo1a6YhQ4Zo3LhxkqRPPvlEYWFhsttP5a+NGzfKZDLp0UcfdZ7jvvvuu6jR9ZLwK9OzQ9LZi6axSjkAAABQyJvx0olDZXJqk6RQwyGTqYjxxuBq0rDVF31uq9Wqw4cPSzoV6jp06KDBgwfrxRdflJ+fn1atWuUMfWvWrNG1115b6ByGYWj27Nl69dVX1aBBA9WrV08LFizQ3XffLUlyOBzq1q2bMjIy9P777+vKK6/Utm3b5Ovrq9atW2vWrFl66qmn9Ouvv556S8HBF1x/SEiI5syZo6ioKG3ZskX333+/QkJCNGHChGJfc91112n//v3avXt3kVPjBwwYoKlTp2rnzp268sorJUk//fSTNm/erIULFzrbpaamql27dgoNDdWCBQs0a9Ys/d///Z/i4uJ06NAhbdq0qdgaqlevri+//FIjRoxQZGRkoefbtm2rjIwM/fjjj2rRooVWr16tiIgIpaWlOdusXr1aEydOvIBP6eIRuN3AZUp5HiPcAAAAQCEnDkkZf5TJqU2nf0qTYRj64osv9PnnnzsXEZs2bZpatGih1157zdkuNjbW+fuePXsUFRVV6FwrV65UZmamunTpIkm666679PbbbzsD98qVK/Xtt9/q559/Vv369SVJdevWdb6+SpUqMplMql69eonfxxNPPOH8PSYmRuPGjdPcuXPPGbjz38OePXuKDNyxsbFq1qyZUlJS9OSTT0qSkpOT1bJlS9WrV8/ZbvHixerZs6ekU1Pqq1evrnbt2qlq1aqKiYnRddddV2wNM2bMUJ8+fVS9enXFxsaqdevW6tmzp7p16ybp1GfSvHlzpaWlqUWLFkpLS9PYsWOVlJSkEydO6Pjx4/rtt98UHx9/4R/WRWBKuRu4TClnWzAAAACgsOBqUkhUmfwYIVFyBFeXUdTzwdVKVOYnn3yi4OBgBQQEqFu3burXr59zCnf+CHdxsrKyFBAQUOj4O++8o379+snP79R4aP/+/bVu3Trt3LnTed6aNWs6w3Zp+vDDD9WmTRtVr15dwcHBeuKJJ7R3795zvsZqtUo6dU96cQYMGKCUlBRJp/448cEHH2jAgAHO59PT07V69Wrn/dt9+/ZVVlaWmjdvrqFDh2rRokXnXIyuUaNG2rp1q7755hsNHjxYhw4dUo8ePXTfffc528THxystLU2GYWjNmjW6/fbb1bBhQ61du1arV69WVFSUrrrqqvN/SJeAEW43YNE0AAAA4DwuYVr3+RgOh9LT0xUaGiqTz6WNObZv316vv/66LBaLoqKinCFZOhNEixMREeG8nzvfkSNHtGjRItlsNpeFyOx2u9555x09++yz5z1vcUwmkwzDcDlW8P7sr7/+WgMGDFBSUpK6dOmiKlWqaO7cuXrhhRfOed4jR45IUpFTufP1799fEydO1A8//KCsrCzt27dP/fr1cz6/bNkyNWrUSNHR0ZKk6Oho/fzzz0pNTdX69es1YsQITZ8+XatXr5bZbC7yGj4+PoqLi1NcXJzGjBmj999/X3fffbcef/xx1alTR+3atdM777yjTZs2yWw2q0GDBmrXrp3S0tJ09OjRMh/dlhjhdguXe7gJ3AAAAECFFRQUpHr16qlWrVouYVuSmjZtqi+++KLY115zzTXatm2by7Hk5GTVrFlTmzZt0saNG50/L7zwgubMmSO73a6mTZtq//792r59e5HntVgszvvEC4qMjNTBgwedj3fs2OEyKr1+/XrVrl1bjz/+uFq0aKGrrrpKe/bsOe9nsHXrVpnNZpfp8merWbOm4uPjlZycrOTkZHXq1EnVqp2ZTVBwOnk+q9Wqbt266cUXX1RaWpq+/vprbdmy5bz15GvUqJEk6eTJk5LO3Mc9c+ZMZ7jOD9xpaWlq167dBZ/7YjHC7QYFp5Qzwg0AAAB4p0mTJqlJkyYaMWKEHnjgAVksFq1atUp9+/Z1bqc1adIkHT16VOHh4ZKkt99+W3369FHjxo1dzhUdHa1Jkybps88+U/fu3XXjjTeqd+/emjFjhurVq6dffvlFJpNJXbt2VUxMjE6cOKEvvvhCzZo1U2BgoAIDA3XTTTfplVdeUatWrWS32zVx4kSX0eKrrrpKe/fu1dy5cxUXF6dPP/1UixYtOu/7XLNmjdq2bXvekfcBAwZo8uTJys3N1cyZM53H8/LytGzZMueK4tKpvcRtNptiY2NVrVo1vf/++7Jarapdu3aR5+7Tp4/atGmj1q1bq3r16tq1a5cmTZqk+vXrq0GDBpKk8PBwNW3aVMnJyXrllVckSTfeeKPuuOMO2Ww2Rri9hdVlhJtVygEAAABvVL9+fS1fvlybNm3Sddddp1atWmnx4sXOkfAmTZroH//4h+bNmydJ+v7777Vp0yb17t270LmqVKmiDh066O2335YkLVy4UHFxcerfv78aNWqkCRMmOEe1W7durQceeED9+vVTZGSkpk2bJkl64YUXFB0drbZt2yohIUHjxo1TYGCg8xq33nqrxo4dq1GjRql58+Zav369c5Gzc5k7d67uv//+87br06ePDh8+rMzMTPXq1ct5fPXq1QoODtY//vEP57GwsDC9/fbb6tq1q5o3b66VK1dqyZIlqlq1apHn7tKli5YsWaIePXqofv36GjhwoBo0aKDly5e7zDyIj4+X3W53jmZfdtllatSokapXr66rr776vO/hUpmMsyf1VyDp6emqUqWKjh8/rtDQUE+XU6yTWTmKTVopSbou5jLNe6CVhytCUWw2m5YuXaqbb7652PtE4Hn0U/lHH1UM9FPFQD9VDPRTyWVnZ2vXrl2qU6dOkYuIlTZHgXu4fS7xHu5L9emnn2r8+PHaunWrx2u5GMuWLdMjjzyizZs3F5pSf6FGjx6tvLw8l9XcpfLVT+f6jpYkhzKl3A3MviaZZMiQiXu4AQAAgEqse/fu2rFjhw4cOOBcMKwiOXnypGbPnn3RYVuSGjdurFatKscgJIHbDUwmkyw+Uo6De7gBAACAym7MmDGeLuGi9enT55LPMXTo0FKopGKoeHMYKqj827gZ4QYAAACAyoHA7SaW0580I9wAAAAAUDkQuN3E7AzcrFIOAAAASFIFXr8ZXq60vpsEbjfJH+HOstn5LxYAAABUavmruWdmZnq4EqBo+d/NS915gEXT3CQ/cNsdhmx2QxY/k2cLAgAAADzE19dXYWFhOnTokCQpMDBQJlPZ/f9jh8Oh3NxcZWdne3y7KRSvPPSTYRjKzMzUoUOHFBYWJl9f30s6H4HbTcw+hqRT/yWSZbPL4sc/dAAAAFRe1atXlyRn6C5LhmEoKytLVqu1TIM9Lk156qewsDDnd/RSELjdxFwgX+fY7JL10qYmAAAAABWZyWRSjRo1VK1aNdlstjK9ls1m01dffaUbb7zxkqcIo+yUl34ym82XPLKdj8DtJpYC/cXWYAAAAMApvr6+pRZuznWNvLw8BQQEELjLMW/sJ+Y1u4mlwCdN4AYAAAAA70fgdpOCU8qzcgncAAAAAODtCNxuUjBwsxc3AAAAAHg/ArebWHzO7L2dzZRyAAAAAPB6BG43YdE0AAAAAKhcCNxuwj3cAAAAAFC5ELjdxOUe7jwCNwAAAAB4OwK3m1gY4QYAAACASoXA7Sauq5QTuAEAAADA2xG43cTCtmAAAAAAUKkQuN3E4ntmWzBWKQcAAAAA70fgdhOXVcoJ3AAAAADg9fw8XYDXczjk++kYdf5zp+QXqel5dyqbRdMAAAAAwOsxwl3WfHxk2vyhrkr/WvE+myWxLRgAAAAAVAYEbnewBEqSrMqRxLZgAAAAAFAZELjdwXwqcAeaTgdu7uEGAAAAAK/n0cAdExMjk8lU6GfkyJGeLKv05QduZUuSstgWDAAAAAC8nkcXTduwYYPs9jOjvVu3blWnTp3Ut29fD1ZVBsyuU8pzGOEGAAAAAK/n0cAdGRnp8vif//ynrrzySsXHx3uoorJhWIJkkmQx2eWnPKaUAwAAAEAlUG62BcvNzdX777+vhx9+WCaTqcg2OTk5ysnJcT5OT0+XJNlsNtlsNrfUeTF8fP2dvwcqR1m59nJdb2WV3yf0TflGP5V/9FHFQD9VDPRTxUA/lX/0UcVQUfqpJPWZDMMwyrCWCzZv3jwlJCRo7969ioqKKrJNYmKikpKSCh1PSUlRYGBgWZd40eJ+f1FRx7+XJLXMfkXpvuH653WMcgMAAABARZOZmamEhAQdP35coaGh52xbbgJ3ly5dZLFYtGTJkmLbFDXCHR0drb///vu8b9STTIuGyW/bQklS+5wXtN8nStsSO3m4KpzNZrNpxYoV6tSpk8xms6fLQTHop/KPPqoY6KeKgX6qGOin8o8+qhgqSj+lp6crIiLiggJ3uZhSvmfPHq1cuVIfffTROdv5+/vL39+/0HGz2VyuO8TuH+z83aoc2eyGTD6+8vNlV7byqLx/n3AK/VT+0UcVA/1UMdBPFQP9VP7RRxVDee+nktRWLhLf7NmzVa1aNXXv3t3TpZQNy5np7vkrlWfnsTUYAAAAAHgzjwduh8Oh2bNna+DAgfLzKxcD7qXPfCZwB5pOB25WKgcAAAAAr+bxwL1y5Urt3btXgwcP9nQpZccc5Pw18PQId1YugRsAAAAAvJnHh5Q7d+6scrJuW9kxFzGlnBFuAAAAAPBqHh/hrgwMS+Ep5VkEbgAAAADwagRud3AZ4c6WJGXbWDQNAAAAALwZgdsdCi6aJka4AQAAAKAyIHC7Q1FTylk0DQAAAAC8GoHbHVg0DQAAAAAqHQK3GxhFbAtG4AYAAAAA70bgdgdWKQcAAACASofA7Q5+VuevVhZNAwAAAIBKgcDtDpbCq5Rns2gaAAAAAHg1Arc7+FllyCRJsppO78Odxz7cAAAAAODNCNzuYDLJ7mORVGAfbka4AQAAAMCrEbjdJM/HX5JkVa4k7uEGAAAAAG9H4HYTe37gNrEtGAAAAABUBgRuN8kP3OzDDQAAAACVA4HbTfKnlAeacmSSgynlAAAAAODlCNxukj/CLUkBymXRNAAAAADwcgRuN8krELgDlaNsG9uCAQAAAIA3I3C7ScERbqsph3u4AQAAAMDLEbjdxO7rOsLNPdwAAAAA4N0I3G5y9pRyAjcAAAAAeDcCt5swpRwAAAAAKhcCt5vYXUa4s5Vtc8jhMDxYEQAAAACgLBG43STPx+L8PVA5kqScPFYqBwAAAABvReB2k7OnlEviPm4AAAAA8GIEbjexn7VomiTu4wYAAAAAL0bgdpOzVymXGOEGAAAAAG9G4HaTIqeU5xK4AQAAAMBbEbjdpOAIt9W5aBqBGwAAAAC8FYHbTYq6hzsrl1XKAQAAAMBbEbjdxO7LKuUAAAAAUJkQuN2ERdMAAAAAoHIhcLuJvYh7uNkWDAAAAAC8F4HbTVxGuE0EbgAAAADwdgRudzH5yDh9H/eZRdMI3AAAAADgrQjc7mQJlCRZlS2Je7gBAAAAwJsRuN3JfCpwn5lSzrZgAAAAAOCtCNzuZM4f4eYebgAAAADwdgRuNzLyR7iVI8ngHm4AAAAA8GIEbnc6fQ+3n8khi/KUnUfgBgAAAABvReB2J79A569W5TDCDQAAAABejMDtTpYzgTtQOaxSDgAAAABejMDtTuYCgduUzaJpAAAAAODFCNxuZJhdp5SzLRgAAAAAeC8CtztZCgbuXKaUAwAAAIAXI3C7k8uUchZNAwAAAABv5vHAfeDAAd11112qWrWqrFarmjRpou+++87TZZWNQlPKCdwAAAAA4K38PHnxo0ePqk2bNmrfvr2WLVumyMhI7dixQ+Hh4Z4sq+yYg5y/BopF0wAAAADAm3k0cD///POKjo7W7Nmzncfq1KnjwYrKlmE5a0q5zS7DMGQymTxYFQAAAACgLHg0cKempqpLly7q27evVq9erSuuuEIjRozQ/fffX2T7nJwc5eTkOB+np6dLkmw2m2w2m1tqvhj5tdl9/J0fuFU5chjSyexc+ft5fGY/dKafyvN3CfRTRUAfVQz0U8VAP1UM9FP5Rx9VDBWln0pSn8kwDKMMazmngIAASdLDDz+svn37asOGDXrooYf0xhtvaODAgYXaJyYmKikpqdDxlJQUBQYGFjpe3lQ7vkmtfn9BkjTT1lsv2ntralyeAj36Zw8AAAAAwIXKzMxUQkKCjh8/rtDQ0HO29WjgtlgsatGihdavX+88Nnr0aG3YsEFff/11ofZFjXBHR0fr77//Pu8b9SSbzaYVK1aoS4NgBXxwuyTpjbxb9M+8BK0df6MuDw3wcIWQzvRTp06dZDabPV0OikE/lX/0UcVAP1UM9FPFQD+Vf/RRxVBR+ik9PV0REREXFLg9OrZao0YNNWrUyOVYw4YNtXDhwiLb+/v7y9/fv9Bxs9lcrjskn6/1TGcE6tQfDvIMnwpRe2VSUb5PlR39VP7RRxUD/VQx0E8VA/1U/tFHFUN576eS1ObRm4fbtGmjX3/91eXY9u3bVbt2bQ9VVMYKrlJuOhW4s1ipHAAAAAC8kkcD99ixY/XNN9/oueee02+//aaUlBS99dZbGjlypCfLKjuWgvtwZ0sSW4MBAAAAgJfyaOCOi4vTokWL9MEHH6hx48Z6+umnNWvWLA0YMMCTZZUdc4FtwcQINwAAAAB4M4+vj33LLbfolltu8XQZ7mG2On/Nn1LOCDcAAAAAeCc2gHYnX3/JdOojt+aPcOc6PFkRAAAAAKCMELjdyWRyLpyWP6WcEW4AAAAA8E4Ebnc7vXCalVXKAQAAAMCrEbjd7fTCaVZGuAEAAADAqxG43c3iOqU8K5fADQAAAADeiMDtbqdHuANMNvnIoew8AjcAAAAAeCMCt7tZzuzFbVUOq5QDAAAAgJcicLvb6VXKpVPTylk0DQAAAAC8E4Hb3QqOcJtyWDQNAAAAALwUgdvdzGcCd6AI3AAAAADgrQjc7mYpOKU8mynlAAAAAOClCNzuZnadUs62YAAAAADgnQjc7mY5a0p5HquUAwAAAIA3InC7W4FVyq3KUTYj3AAAAADglQjc7lZwhNvEtmAAAAAA4K0I3O521irlBG4AAAAA8E4EbncruGga24IBAAAAgNcicLuby5TybAI3AAAAAHgpAre7mQvuw50jm92Qzc5K5QAAAADgbQjc7lZghDtAuZLEKDcAAAAAeCECt7uZXVcpl6RsGyPcAAAAAOBtCNzuZnGdUi4xwg0AAAAA3ojA7W4uq5RnSxJbgwEAAACAFyJwu1sRU8qzcgncAAAAAOBtCNzu5uMj+VklndqHW2JKOQAAAAB4IwK3J5xeqTz/Hm6mlAMAAACA9yFwe8LpvbjPrFJO4AYAAAAAb0Pg9oTTI9xnppSzLRgAAAAAeBsCtyeYC04pN5hSDgAAAABeiMDtCaf34vYxGfKXjVXKAQAAAMALEbg9wWx1/hqobEa4AQAAAMALEbg9oeBe3MpRDoEbAAAAALwOgdsTTk8plySrKYcRbgAAAADwQgRuTzhrhJvADQAAAADeh8DtCZYCgduUo6xctgUDAAAAAG9D4PYE85kp5QHKUXYeI9wAAAAA4G0I3J5gcZ1Sns22YAAAAADgdQjcnmA+a0o593ADAAAAgNchcHtCwVXKWTQNAAAAALwSgdsTzlqlPNvGomkAAAAA4G0I3J5w1irl2YxwAwAAAIDXIXB7gvmsKeUsmgYAAAAAXofA7Qkuq5Rnsy0YAAAAAHghArcnFBzhNjHCDQAAAADeiMDtCWftw52T55DDYXiwIAAAAABAafNo4E5MTJTJZHL5adCggSdLco+zVimXxLRyAAAAAPAyfp4uIDY2VitXrnQ+9vPzeEllr0DgtppOB26bQ4EWTxUEAAAAAChtHk+3fn5+ql69+gW1zcnJUU5OjvNxenq6JMlms8lms5VJfaUhv7YzNZrk5+MnkyPPOcKdkZmtEIvJQxVCKqqfUB7RT+UffVQx0E8VA/1UMdBP5R99VDFUlH4qSX0mwzA8dvNwYmKipk+fripVqiggIECtWrXS1KlTVatWrWLbJyUlFTqekpKiwMDAIl5Rft28+QGZ7Zna6aihDrkv6LHmebrc6umqAAAAAADnkpmZqYSEBB0/flyhoaHnbOvRwL1s2TKdOHFCV199tQ4ePKikpCQdOHBAW7duVUhISKH2RY1wR0dH6++//z7vG/Ukm82mFStWqFOnTjKbzZIkvxcby3TiTx00LlOrnFf08fDrFRtVft9DZVBUP6H8oZ/KP/qoYqCfKgb6qWKgn8o/+qhiqCj9lJ6eroiIiAsK3B6dUt6tWzfn702bNlXLli1Vu3ZtzZs3T0OGDCnU3t/fX/7+/oWOm83mct0h+VzqtJzaGixQ2ZKkPMNUId5DZVBRvk+VHf1U/tFHFQP9VDHQTxUD/VT+0UcVQ3nvp5LUVq62BQsLC1P9+vX122+/ebqUsnd6a7AA5UqSsmysUg4AAAAA3qRcBe4TJ05o586dqlGjhqdLKXvmUyPc/qY8+cqurFwCNwAAAAB4E48G7nHjxmn16tXavXu31q9fr9tuu02+vr7q37+/J8tyD4vrXtyMcAMAAACAd/HoPdz79+9X//79dfjwYUVGRuqGG27QN998o8jISE+W5R4F9+JWjnJsDg8WAwAAAAAobR4N3HPnzvXk5T3r9KJpkhRoymaEGwAAAAC8TLm6h7tSMTOlHAAAAAC8GYHbUwqMcFuVo2wCNwAAAAB4FQK3pxQc4TYxwg0AAAAA3obA7SlnrVKezbZgAAAAAOBVCNyeYnadUs4INwAAAAB4FwK3p1hcp5Rnsy0YAAAAAHgVArenmK3OX61iWzAAAAAA8DYEbk8pMKU8kFXKAQAAAMDrELg95awp5VksmgYAAAAAXoXA7SlnLZqWnUfgBgAAAABvQuD2lLO2BWOEGwAAAAC8C4HbU8xnAreVVcoBAAAAwOsQuD3FctaUchZNAwAAAACvQuD2FPNZU8oJ3AAAAADgVQjcnmK2SjJJOr1Kuc0uwzA8WxMAAAAAoNQQuD3FZHKOcluVI8OQcvK4jxsAAAAAvAWB25NOr1QeqBxJUg4LpwEAAACA1/C7mBcdO3ZMixYt0po1a7Rnzx5lZmYqMjJS11xzjbp06aLWrVuXdp3eKX+E23QqcGfZ7KoisycrAgAAAACUkhKNcP/xxx+67777VKNGDT3zzDPKyspS8+bN1aFDB9WsWVOrVq1Sp06d1KhRI3344YdlVbP3OL1Sef4INwunAQAAAID3KNEI9zXXXKOBAwfqcR5pCAAAIABJREFU+++/V6NGjYpsk5WVpY8//lizZs3Svn37NG7cuFIp1CudHuEONOXIJIeycgncAAAAAOAtShS4t23bpqpVq56zjdVqVf/+/dW/f38dPnz4korzepYzW4MFKFfZeQRuAAAAAPAWJZpSfr6wfantKx1zkPPXQOUomxFuAAAAAPAaJRrhTk1NVbdu3WQ2m5WamnrOtrfeeuslFVYpmK3OX62n9+IGAAAAAHiHEgXuXr166c8//1S1atXUq1evYtuZTCbZ7YTH8yowpTxQBG4AAAAA8CYlCtwOh6PI33GRzp5Szj7cAAAAAOA1SnQP9/kcO3asNE/n/QqMcDOlHAAAAAC8y0UH7ueff95lr+2+ffvqsssu0xVXXKFNmzaVSnFez2WEO5tF0wAAAADAi1x04H7jjTcUHR0tSVqxYoVWrlypzz77TN26ddP48eNLrUCvVnCEW7nKZoQbAAAAALxGie7hLujPP/90Bu5PPvlEd9xxhzp37qyYmBi1bNmy1Ar0amamlAMAAACAt7roEe7w8HDt27dPkvTZZ5+pY8eOkiTDMFih/EJZXBdNI3ADAAAAgPe46BHu22+/XQkJCbrqqqt0+PBhdft/9u48Tqrqzv//+9Ze1XuzLw0C7YoiguIDN1BBFOMymm8WjNEsk0nUmUQnE3Wc/KImqGMyRhO3xA0nIzEmouNEVBBFxCUigiIghh1kp6H3rvX+/rjV1VXd1d1VTVfX0q/n43EffevW0qc8kPC+53POuegiSdKqVatUXV3daw0saM7EbcEOE7gBAAAAoGD0OHD/+te/1pgxY7R9+3bde++9Ki4uliTt3r1b1113Xa81sKAlrFLeoj1sCwYAAAAABaNHgbuurk5/+9vfdNxxx+mb3/ymBg0aFHvuxhtv7LXGFbx2+3A3s0o5AAAAABSMtAP36tWrNXv2bO3du1emaaqkpETPPfecZs2alYn2FTZXYkk5c7gBAAAAoHCkvWjazTffrDFjxmj58uVauXKlzj//fN1www2ZaFvhY5VyAAAAAChYaY9wr1y5UosWLdKkSZMkSU8++aQqKytVV1en0tLSXm9gQWu3SrmfwA0AAAAABSPtEe6amhqNHDky9ri8vFxFRUU6ePBgrzasX4gf4aakHAAAAAAKSo8WTVu3bp327NkTe2yaptavX6/6+vrYtQkTJhx56wpd/LZglJQDAAAAQEHpUeA+//zzZZpmwrUvfelLMgxDpmnKMAyFw4THbtkdkt0lhQPRVcrZFgwAAAAACkXagXvLli2ZaEf/5fRJ4YC8amEONwAAAAAUkLQD9+jRozPRjv7LVSS1HKakHAAAAAAKTFqLpm3fvj2tD//iiy/Sen2/FJ3H7ZVfoYipYJiycgAAAAAoBGkF7tNOO03/9E//pBUrVnT6mtraWj322GM68cQT9fzzzx9xAwueywrcPvklmWphlBsAAAAACkJaJeXr1q3T3LlzNXPmTHk8Hk2ePFnDhw+Xx+PRoUOHtG7dOq1du1aTJk3Svffeq9mzZ2eq3YXDae3F7TAicimk5mBYJR5nlhsFAAAAADhSaY1wDxgwQPfdd592796tBx98UEcffbQOHDigv//975Kkq666SitXrtR7772Xdti+5557ZBiGfvSjH6X1vrznatsazCO/WlipHAAAAAAKQo+2BfN6vfryl7+sL3/5y73SiBUrVuh3v/td/9y7O34vbrFwGgAAAAAUirRGuOPV1taqpqamw/WamhrV1dWl/DkNDQ266qqr9Nhjj6mioqKnzclfrqLYqc/wM4cbAAAAAApEj0a4JelrX/uaLrnkEl133XUJ15977jm99NJLWrhwYUqfc/311+viiy/WjBkz9Itf/KLL1/r9fvn9/tjj1mAfDAYVDAbT/AZ9p7Vtydpos3tkj5575Vd9sz+nv0sh66qfkDvop9xHH+UH+ik/0E/5gX7KffRRfsiXfkqnfYZpmmZPfkllZaXeeecdHX/88QnXP/vsM5155pk6ePBgt5/x7LPPau7cuVqxYoU8Ho+mT5+uiRMn6v7770/6+ttvv1133HFHh+vz58+Xz+dL8o7cd8IXf9TR+16RJP0///+nSccerRMqetQlAAAAAIAMa2pq0pw5c1RbW6vS0tIuX9vjEW6/369QKNThejAYVHNzc7fv37Fjh374wx9q8eLF8ng8Kf3OW2+9VTfddFPscV1dnaqqqnTBBRd0+0WzKRgMavHixZo5c6aczsQVyG3L1kjRwO0z/Dpx4iRdOH5INprZ73XVT8gd9FPuo4/yA/2UH+in/EA/5T76KD/kSz+lM4W6x4F7ypQp+v3vf6/f/va3CdcfffRRTZ48udv3r1y5Uvv27dOkSZNi18LhsJYtW6YHH3xQfr9fdrs94T1ut1tut7vDZzmdzpzukFZJ2+kpiZ165VfIVF58l0KWL3+e+jv6KffRR/mBfsoP9FN+oJ9yH32UH3K9n9JpW48D9y9+8QvNmDFDH3/8sc4//3xJ0pIlS7RixQotWrSo2/eff/75WrNmTcK1b33rWzruuON08803dwjbBSthlfIWNbMtGAAAAAAUhB4H7jPPPFPvv/++7r33Xj333HPyer2aMGGCnnjiCR199NHdvr+kpEQnnnhiwrWioiINGDCgw/WC1m6VcrYFAwAAAIDCkHbgjkQi+uUvf6mXXnpJgUBA5513nh5//HF5vd5MtK/wOdv+u3nFtmAAAAAAUCjSDtxz587V7bffrhkzZsjr9eo3v/mN9u/fryeffPKIG7N06dIj/oy844wb4SZwAwAAAEDBsKX7hv/+7//Www8/rNdee00vvvii/u///k/PPPOMIhHmHveIq20Ot9fwqzlA4AYAAACAQpB24N6+fbtmz54dezxjxgwZhqFdu3b1asP6jYRF05jDDQAAAACFIu3AHQqFOuyb7XQ6FQwGe61R/Uq7RdNaglQKAAAAAEAhSHsOt2mauvbaaxP2w25padH3v/99FRW1hccFCxb0TgsLXdwIt1ctzOEGAAAAgAKRduC+5pprOlz7xje+0SuN6ZdciYumUVIOAAAAAIUh7cD91FNPZaId/Vf8CLcRYNE0AAAAACgQac/hRi9zuGUaVjd45VdLiMANAAAAAIWAwJ1thiEjuhe3T2wLBgAAAACFgsCdC6J7cXsNP4umAQAAAECBIHDngug8bp9aWDQNAAAAAAoEgTsXuNpKytmHGwAAAAAKA4E7F0RHuD1GUP5gMMuNAQAAAAD0BgJ3LnC1bQ1mDzUrHDGz2BgAAAAAQG8gcOeC6CrlUmtZOfO4AQAAACDfEbhzgdMbO2WlcgAAAAAoDATuXBBXUu6Tn5XKAQAAAKAAELhzQUJJeQsj3AAAAABQAAjcuSBuhNsqKWdrMAAAAADIdwTuXOCkpBwAAAAACg2BOxe42krKvfKrOUDgBgAAAIB8R+DOBfEj3AYj3AAAAABQCAjcuaDdCDeLpgEAAABA/iNw54K4EW4CNwAAAAAUBgJ3LnC1KylnDjcAAAAA5D0Cdy5I2Ifbr2a2BQMAAACAvEfgzgUuSsoBAAAAoNAQuHNBu1XKCdwAAAAAkP8I3LnA1b6knMANAAAAAPmOwJ0LElYpb2HRNAAAAAAoAATuXNC+pDzEomkAAAAAkO8I3LnAZpPp8EiyFk1jhBsAAAAA8h+BO1dER7l9rFIOAAAAAAWBwJ0roluDsUo5AAAAABQGAneOMJzWSuVeVikHAAAAgIJA4M4VrraS8uZAKMuNAQAAAAAcKQJ3roiOcNsMU5FAc5YbAwAAAAA4UgTuXOFq2xrMFiJwAwAAAEC+I3Dniri9uBUkcAMAAABAviNw5wpXUezUCDXJNM0sNgYAAAAAcKQI3LkiboTbJ7/8oUgWGwMAAAAAOFIE7lzhSgzc7MUNAAAAAPmNwJ0rnG0l5V6jhb24AQAAACDPEbhzRbsR7uYAgRsAAAAA8hmBO1fEz+E2/IxwAwAAAECeI3DnirhVyr3yqyXIomkAAAAAkM+yGrgfeeQRTZgwQaWlpSotLdXUqVP1yiuvZLNJ2eNk0TQAAAAAKCRZDdwjR47UPffco5UrV+rDDz/Ueeedp8suu0xr167NZrOyo31JOXO4AQAAACCvObL5yy+55JKEx3PnztUjjzyi999/X+PHj89Sq7IkbtE0r/xqCRG4AQAAACCfZTVwxwuHw/rzn/+sxsZGTZ06Nelr/H6//H5/7HFdXZ0kKRgMKhgM9kk7e6K1bV220XDJGT31qUX765pz+jsVopT6CVlHP+U++ig/0E/5gX7KD/RT7qOP8kO+9FM67TNM0zQz2JZurVmzRlOnTlVLS4uKi4s1f/58zZ49O+lrb7/9dt1xxx0drs+fP18+ny/JO/JHcctunb/+ZknS8+Gz9FTx9/WPx7FwGgAAAADkkqamJs2ZM0e1tbUqLS3t8rVZD9yBQEDbt29XbW2t/vKXv+jxxx/XW2+9pRNOOKHDa5ONcFdVVenAgQPdftFsCgaDWrx4sWbOnCmn05n8RXW75PztBEnSK+HT9CPzX7Xi1nPlddn7sKX9W0r9hKyjn3IffZQf6Kf8QD/lB/op99FH+SFf+qmurk4DBw5MKXBnvaTc5XKpurpakjR58mStWLFCDzzwgH73u991eK3b7Zbb7e5w3el05nSHtOqynb62jvLJL38oog+21WrGCUP6qHVolS9/nvo7+in30Uf5gX7KD/RTfqCfch99lB9yvZ/SaVvO7cMdiUQSRrH7DWfcPtyG9f1fX783W60BAAAAAByhrI5w33rrrbrooos0atQo1dfXa/78+Vq6dKlee+21bDYrOxwuyeaQIiEVGQFJ0uvr9ykSMWWzGVluHAAAAAAgXVkN3Pv27dM3v/lN7d69W2VlZZowYYJee+01zZw5M5vNyh5nkeSvVaUrJPmlAw1+fbzzsE4ZVZHtlgEAAAAA0pTVwP3EE09k89fnHpdP8teqzB6IXXp9/V4CNwAAAADkoZybw92vOa2tzTzyy4hWkb++bl8WGwQAAAAA6CkCdy5xWYHbFmzSxKpySdKGvfXaUdOUzVYBAAAAAHqAwJ1LWlcqDwc087gBscusVg4AAAAA+YfAnUuiI9ySdEF1SeycwA0AAAAA+YfAnUucbYF7XLmhqkqvJOlvm2tU2xzMVqsAAAAAAD1A4M4lrqLYqRFs1ozjh0iSQhFTb32+P1utAgAAAAD0AIE7lzi9beeBRs2MBm5Jen0dZeUAAAAAkE8I3LnE2TbCrWCTThtTqRKPtVX6mxv2KRiOZKlhAAAAAIB0EbhzSdyiaQo0ymm36dxjB0uS6ltCWrGlJksNAwAAAACki8CdS+IWTVPQ2nt7xglxZeXr9/V1iwAAAAAAPUTgziVxi6bJ3yBJmnbMIDlshiRp8fo9Mk0zGy0DAAAAAKSJwJ1Lyka2nW97x7rkder0sZWSpB01zfr7voZstAwAAAAAkCYCdy4Ze27bwmnr/08KBSRJ5x/XVla+mNXKAQAAACAvELhzicsnHTfbOm85LG1+U5Ji+3FL0uvrCdwAAAAAkA8I3Llm/BVt558ukCSNGuDTsUNKJEmrdxzWvvqWbLQMAAAAAJAGAneuqT5fcpdZ55+9LAWtcD3jBGt7MNOU3vyM1coBAAAAINcRuHONwy0d/yXrPFAvbVwsKbGsfPE6AjcAAAAA5DoCdy46Mb6s/HlJ0skjyzWw2C1JWr5xv1qC4Wy0DAAAAACQIgJ3LhozTfINsM4/f00KNMpmMzTjeKusvCUY0TsbD2SxgQAAAACA7hC4c5HdKR1/qXUebJI2vCJJOp/VygEAAAAgbxC4c9WJV7adr31BknRW9UC5HVaXvb5+nyIRMxstAwAAAACkgMCdq0afIRVHR7T/vkhqqZXXZdfZRw+UJO2v9+uTL2qz2EAAAAAAQFcI3LnKZpfG/4N1Hg5Iny2UlLha+evrKCsHAAAAgFxF4M5l4zuuVn5edOE0iXncAAAAAJDLCNy5bORpUlmVdb75TampRoNLPJpYVS5J+mxPvXbUNGWxgQAAAACAzhC4c5nNJo2/3DqPhKT1L0mSZp7QVla+hFFuAAAAAMhJBO5cF79a+acLJLWbx71+X1+3CAAAAACQAgJ3rhs2UaoYY51vfVtq2KdjhhRrZIVXkvT+5oOqawlmsYEAAAAAgGQI3LnOMNpGuc2ItO5/ZRhGbJQ7FDH11ob9WWwgAAAAACAZAnc+OLHjauXx87hZrRwAAAAAcg+BOx8MPkEadJx1vv09qfYLTRlTqRKPQ5L05mf7FAxHsthAAAAAAEB7BO58YBiJe3KvfUFOu03Tj7X25K5rCenDrYey1DgAAAAAQDIE7nwRX1a+tnW18sGxS5SVAwAAAEBuIXDni4FHS0NPss6/WCnVbNH0YwbLYTMkWYHbNM0sNhAAAAAAEI/AnU/i9+Reu0BlPqdOO6pSkrTtYJM27mvIUsMAAAAAAO0RuPPJ+H9oO//0BUnSjLjVyhdTVg4AAAAAOYPAnU8qjpJGnGqd710j7f88cR73OgI3AAAAAOQKAne+abd42ugBRTpmSLEkadWOwzrQ4M9SwwAAAAAA8Qjc+Wb8P0iyFkrTp89LpqkZx1tl5aYpvfHZvuy1DQAAAAAQQ+DON6XDpVFTrfMDn0t71ybM46asHAAAAAByA4E7H8WXlX/6vCaOLNfAYpck6e2/H1BLMJylhgEAAAAAWhG489EJl0lGtOvWLpDNkM47zlo8rTkY1rubDmSxcQAAAAAAicCdn4oHS2POsc4PbZV2fRSbxy1Ji9cxjxsAAAAAso3Ana/Gx5eVL9BZRw+U22F155L1exWJmFlqGAAAAABAynLgvvvuu3XaaaeppKREgwcP1uWXX64NGzZks0n54/hLJJvDOl/7gnwOm86qHihJ2lfv15ovarPYOAAAAABAVgP3W2+9peuvv17vv/++Fi9erGAwqAsuuECNjY3ZbFZ+8FVK486zzuu+kHZ+kLha+XpWKwcAAACAbMpq4H711Vd17bXXavz48Tr55JM1b948bd++XStXrsxms/LHiVe2nX/6vM6PLpwmSa+vZx43AAAAAGSTI9sNiFdba5VBV1ZWJn3e7/fL7/fHHtfV1UmSgsGggsFg5hvYQ61t6/U2jrtADrtbRtgvc+2Lqjj/55owslSf7KzT+t112rq/TiPKvb37OwtYxvoJvYp+yn30UX6gn/ID/ZQf6KfcRx/lh3zpp3TaZ5immROra0UiEV166aU6fPiwli9fnvQ1t99+u+64444O1+fPny+fz5fpJuak0zY/oOG1VkXAO9W3aH7teL28wy5JuvKosM4ZlhPdCwAAAAAFoampSXPmzFFtba1KS0u7fG3OBO4f/OAHeuWVV7R8+XKNHDky6WuSjXBXVVXpwIED3X7RbAoGg1q8eLFmzpwpp9PZq59trHtRjhe+K0kKn/JNrZt0hy556D1J0pnjBmjetZN79fcVskz2E3oP/ZT76KP8QD/lB/opP9BPuY8+yg/50k91dXUaOHBgSoE7J0rKb7jhBv31r3/VsmXLOg3bkuR2u+V2uztcdzqdOd0hrTLSzuNnS3/1ScEm2T/7P5148X9pRLlXXxxu1gdba9Qclko9uf/fJpfky5+n/o5+yn30UX6gn/ID/ZQf6KfcRx/lh1zvp3TaltVF00zT1A033KAXXnhBb7zxhsaMGZPN5uQnV5F07EXWefMhGVuWaWZ0tfJg2NSyz/dnsXEAAAAA0H9lNXBff/31+p//+R/Nnz9fJSUl2rNnj/bs2aPm5uZsNiv/jL+i7fzT5zXj+LjtwdaxPRgAAAAAZENWA/cjjzyi2tpaTZ8+XcOGDYsdf/rTn7LZrPxTPUNyR+cOfPZXTakqUonbmi3w5ob9CoUjWWwcAAAAAPRPWS8pT3Zce+212WxW/nF6pOMuts79dXJtfUPTjh0kSaptDurDbYey2DgAAAAA6J+yGrjRi068su380wWxedwSZeUAAAAAkA0E7kIxdrrkrbDON7yi6WOKZbcZkqTF6/cqR3Z/AwAAAIB+g8BdKOxO6fhLrfNgo8p2vqHTjrIC+LaDTdq0vyGLjQMAAACA/ofAXUhO7Hy18sXr9mWhQQAAAADQfxG4C8lRZ0tFg63zzxfpgmpf7KnX1zOPGwAAAAD6EoG7kNjs0gmXWedhv0bte0tHDy6WJH20/ZAONviz2DgAAAAA6F8I3IUmYbXy5zUjulq5aUo//+s61bcEs9QwAAAAAOhfCNyFpup0qWS4db7pDV16tFeGtVi5Xly9S7N+vUxLNzCfGwAAAAAyjcBdaGy2tsXTIkEdX/uW/vOKCfK57JKkXbUtuvapFfrxnz9WbROj3QAAAACQKQTuQjQ+frXyBfrKaVV67Ufn6KzqgbHLf1m5UzN//ZYWr2MxNQAAAADIBAJ3IRoxSSofbZ1veUtq2K+qSp/+8J0p+s8rT1KJ2yFJ2lfv1z/+94f6lz+uUk1jIIsNBgAAAIDCQ+AuRIbRVlZuRqT1/xu9bOirp43SopvO0bnHDoq9/KWPd2nmfW/p5U92Z6O1AAAAAFCQCNyFKmG18gUJTw0r8+rJa0/TfV85WWVepyTpYGNA18//SD/4n5XaV9/Sly0FAAAAgIJE4C5UQ06UBhxtnW97V6rblfC0YRi6YtJILb7pHM0aPyR2/ZVP9+iCXy/TC6t2yjTNvmwxAAAAABQUAnehMoy4UW5TWvti0pcNLvHo0W9M1kNzJmlAkUuSdLgpqBv/9LG++/SH2lPLaDcAAAAA9ASBu5CdGLda+doFnb7MMAxdPGGYFt14ji49eXjs+pLP9mnmfW/pTyu2M9oNAAAAAGkicBeyQcdapeWStHOFdGhrly8fUOzWb75+in5/9WQNLnFLkur9Id38/Bpd/cQH2lHTlOEGAwAAAEDhIHAXuvH/0HY+70tWaXk3o9UXjB+qxTdO05cnj4xdW77xgC68f5n+8N5WRSKMdgMAAABAdwjchW7iHMlbYZ3X7pD+fI309CXS3rVdvq3M59Sv/t/Jmvet0zSszCNJagyE9dP/XauvP/a+th5ozHTLAQAAACCvEbgLXelw6TuLpXHntV3b+rb06FnSwn+Tmmq6fPv0Ywdr0Y3naM7po2LX/ralRhc+sEy/X7ZJWw40KhSOZKr1AAAAAJC3HNluAPrAwKOlbyyQNrwivXarNZfbjEgf/F5a8xfpvP+QJl8r2exJ317icequfzhJXzppmG5e8Il21DSrJRjRXQs/010LP5PTbuioAUWqHlyscYOKYz/HDipSkZs/YgAAAAD6J9JQf2EY0nGzrZHu9x+Slv1KCjZJzTXSyzdJK5+SLrpXGn1Gpx9xRvVAvfrDc/TL1zbo6fe2xqaCB8Om/r6vQX/f19DhPcPLPBoXDeDWTyuYDyp2yzCMDH1ZAAAAAMg+And/4/RIZ/+rNOFr0us/k9b82bq+Z4301EXSiV+WZt4plY1I+vYit0O3XzpeV04aqcXr9mjT/kZt3NegLQcaFUhSWr6rtkW7alv09t8PJFwv8ThiI+Fto+JFGlXpk8POTAcAAAAA+Y/A3V+VjZCufFw69TvSKz+R9nxiXf/0L9KGhdLZN0lT/9kK6EmcNLJMJ40siz0OR0ztqGnSpv0N2rS/QRv3NcTCeG1zsMP761tCWrX9sFZtP5xwvbU8PRbCBxfFQjnl6QAAAADyCQmmvxs9VfreUumj/5aW3GmVmAebpDd+IX30B+nCu6VjZ1sl6V2w2wwdNbBIRw0s0vnHD4ldN01TBxsD2rSvQRv3N2jTvsbozwZ9cbi5w+cklKe3W0h9WJknblS8SOMGF6t6ULEGlVCeDgAAACD3ELhhLZZ26rek8ZdLb94trXhcMsPS4W3Ss3Osed8X3iMNOjbtjzYMQwOL3RpY7NbpYwckPNcUCGnz/kZrVDw6Ir5pf4M2H2hUINSxPH13bYt2d1KeHr9YW+s8ccrTAQAAAGQTgRttvBXS7HutFctfvVnassy6vukN6ZEzpCn/JE2/WfKUdfkxqfK5HDpxRJlOHJH4eeGIqZ2HmqJl6W2j4l2Vp6/ecVird3Renj5ucFHc6unFKqY8HQAAAECGkTrQ0ZATpG++JK1/SXrtNql2hxQJWaubr3lOOv9n0sSrJFtmRo/tNkOjBxRp9IDOy9Nb54e3zhfvSXl6/GJt4wYVa3SlJ7byOgAAAAAcKQI3kjMM6YTLpOqZ0ru/kZb/Wgq1SI37pZdukD58wiozHzklY8G7Y5M6L09vDoS1+UDbYm2bomG8u/L05RsTy9O9drue2vk3VQ8usUbFo9uZjaY8HQAAAECaCNzomssnTb9FmjhHWvRTad2L1vVdq6QnZ0l2t1Q5RqocGz3izktHSva++SPmddk1fniZxg9PXp4eWzm9m/L05rChj3fW6uOdtQnXnXZr1L11fnjr6Djl6QAAAAA6Q1JAaspHSV95WtrytvTKzdK+aI122C/t/8w62rM5pYrRcWE87iirkhyujDc7vjz9vOMSy9NrGgMJ25dt3FenT7cfUI2/44rnwbAZfU2DXlu7N+G5oaWeWGl6bOG2wcUazOrpAAAAQL9G4EZ6xpwt/dMyadUfpM9flWq2SIe2SOFAx9dGgtLBjdbRnmGzQneyMF5xVKf7f/cWwzA0oNitAXHl6cFgUAsXLtS5M2ZpR62/rTw9uop6Z+Xpe+patKeuY3l6iduhsdGty1r3E29dPd1JeToAAABQ8AjcSJ/dYW0jduq3rMeRsFS3S6rZnHgc2mr9DDZ1/AwzYm07dnibtPnNdk8aUumIdqXqcSXrrqKMfr1UytM37YtbtG1/gw43JVk93R/SxzsO6+Mkq6e3lqcnbGc2mPJ0AAAAoJDwr3scOZtdKq+yjrHTEp8zTalhb8cwXrNZOrhZCtQn+UBTqttpHVvf7vh08dCXQnOaAAAgAElEQVQkc8ajP3tpy7JkEsvT47+iVZ7efuX0Tfut1dPbr3weX54udSxPj1+srZrydAAAACBvEbiRWYYhlQy1jtFnJD5nmlLTweRhvGaz1Hwo+Wc27LGO7e92fM43IHmZeuVYa5/xDITW+PL0KWMqE55rXT09PoynUp7+zsaDCddby9MT5okPKtboAZSnAwAAALmKwI3sMQypaKB1VE3p+HzzIWuOeM3muJ/Ro3Ff8s9sOmgdO1d0fM5T1nkYLxrUu98tqqvy9C8ONWvj/npt2tcYGxVPtzzdYTM0eoAvIYRbq6cXqcTjzMh3AgAAAJAaAjdyl7dCGlEhjZjU8Tl/fccQ3vq4flfyz2uptbYz27Wq43OuYjnKj9JpAa9sb3woDaxuC+Mlw3p9r3G7zdCoAT6NGuBLKE+XpIMN/sQR8WgYT1aeHoqY0YXdGtVZeXr8PPFqytMBAACAPkPgRn5yl0jDJlhHe4GmtgXbDrUL5Yd3SDKTvKdBxr5PNVyS3ms3Ou7wSBXt5orHtjcbac1h70WplKdvio6G96Q8vdjtsBZsiwvhlKcDAAAAvY/AjcLj8klDTrCO9kJ+6fD2pHPGzUPbZJjhJO9pkfavt472bE5rG7Nkq6mXj5LsvVfW3V15evxibV2Vpzf4Q/p4Z60+3lmbcL21PL39yunjKE8HAAAAeoTAjf7F4ZYGHm0d7YRamrT0pf/RuRNGy1G3PbFMvcu9xv9uHe0Z0dXbk80ZLx/da3uNx5enn3vc4ITnahoDHVZO37S/QTsPdV2evmhdYnn6kFJ3h9L0cYOKNaSU8nQAAACgMwRuoJXdqSb3EJnjzpOc7UZ0O9trvDWQh5o7fp4ZtkrbD22VNr3R7knDKkdPttd4xVG9ttd4ZZFLU8ZUJi1P33KgMVaW3hrIOytP31vn1946v97d1El5emw03ArjlKcDAAAABG4gNd3tNV6/p5PtzbZ0vtd47Q7r2LKs49MlwzrOGW+dR+4pPeKv43XZdcLwUp0wPPGz4svT25eoH0qzPH3UAF+H/cQpTwcAAEB/QuAGjpRhSKXDrOOoMxOf6+le4/W7rWPbOx2f8w3sZHuzMZKvsuPr09BdeXoshLcu2tZFefrm/Y3avL9R6qY8vfWc8nQAAAAUGgI3kEnd7TXeVBNdSb39Fmebpcb9yT+z6YB17Pyg43Oe8i72Gh9otaeHKotcqiyq1GlHJYb6lmBYm/c3dhgR33KgUf40ytOLXPZ2o+HFqh5cpFGVRXI5KE8HAABA/iFwA9nkq7SOEZM7PtdSlySMd7fX+GFp10fW0Z6rRKo8KnkYLx7a473GPc7Oy9N3HW5Osmhbo2oaOy5A1xgI65Odtfqkk/L01pHwoyo92l5raNyeeg0u86nc5yKQAwAAICdlNXAvW7ZMv/zlL7Vy5Urt3r1bL7zwgi6//PJsNgnIHZ5SadjJ1tFe/F7j7eeM13a213i9tGeNdbTn8MbNF2+3kFvpiB7tNW63Gaqq9Kmqsvvy9E37rRL17srTF8fK0+16aN17sdeUuB2qKHJFR+FdqvC5VFnkVEWRSwNij13Wa3wulXmdstkoXwcAAEBmZTVwNzY26uSTT9a3v/1tXXHFFdlsCpBfuttr/NC2xCB+qHV7s23W6ukd3tMs7VtnHe3ZXUn2Go+G8rKqHu01nkp5etuoeKM2729IWp7eqt4fUr0/pO01TSn9fpshVfjaAnhFkVOVRW4rpPvagntlXFj3uezMMQcAAEBashq4L7roIl100UXZbAJQeBxuadAx1tFeOGiNgMeXp8dC+dbke42HA9KBz62jPcMulY9KXqZeMdpqSxo6K0+PREx9cbhZG/c36O976vS3j9ercliVDjWFdKgpoEONAdU0BXQ4yUrqyURM6WBjQAeTlLZ3xuWwJRktjwvqsfBujapT6g4AAIC8msPt9/vl9/tjj+vq6iRJwWBQwWBq/9DOhta25XIb0Y/6qaTKOka3294sEpbqd8mo2SLjkDUqbp1vkQ5tldHpXuNbrGPTksSnonuNmxVjZFaMkSrHyKwYa51XjJacvrSaPbTEqaElFTq9qlhDDq/VzJnHyNluv/RQOKLa5qBqmoLRIB5UTfTnoaaADkWv18Q9bgokGfFPIhCKaHdti3bXtqTc5mK3QxU+pzWC7msL6RU+lyp8zugIevRxkVNlnsIode83f5fyHP2UH+in/EA/5T76KD/kSz+l0z7DNNvPmMwOwzC6ncN9++2364477uhwff78+fL50vvHO4A0mKY8ocMq8u+NHvvizvfKGUk9hLZqdlao0T3EOlyDo+fWz5Ddm4EvkVwgLDWFpIaQ1BA01BiSGoNSQ8iI/kx83BiSwmZmQrEhUz6HVOyUih1SkdNUUfRxkcOM/rSuF0evu2xHtPg8AAAA0tTU1KQ5c+aotrZWpaWlXb42rwJ3shHuqqoqHThwoNsvmk3BYFCLFy/WzJkzO4zIIXfQTz1kmlLTAWskPDo6nnDeUtv9Z7T/yKJB0ZFwa3TcrGw9H6ugoyir/WSaphr8IWsUvTH5qHnr9Zroz8PNmbtL63LYYqPksTnocaPmrXPUW0fVK/qg1J2/S/mBfsoP9FN+oJ9yH32UH/Kln+rq6jRw4MCUAndelZS73W653R3nhDqdzpzukFb50s7+jn7qAddwqXy4NObMjs811bTNFz+0JaW9xo3G/TIa9yfda9zhrdC5ZpE8Bx+RzTdA8pZL3grr8MSdxx/ukl4dBq50uVRZkvrrW0vdDzUFdLAhkBDQaxrbjtbHhxoDakyj1L11b/NUta7qHitxb13NPW4eemzhuCNY1Z2/S/mBfsoP9FN+oJ9yH32UH3K9n9JpW14FbgB5qHWv8ZFd7TW+Ocle47uTfpzRfEilOiRt35l6G2yOzsN40iP6Wk9Zj7ZEa89ht2lAsVsDit2qHtz96yVrxfa2AG7NRa9p8MdG1mtaF4uLC+vBcGoFSz1Z1b08OkI+oMgdXdXdlbCie2tYryxyqdhldNjeDQAAoD/KauBuaGjQxo0bY4+3bNmi1atXq7KyUqNGjcpiywD0iS73Gm9st9e4FcTNms0K1++VI5L6CuOKhKSmA9aRFsMK3SkH9bjA3oPt0uJ5nHYNK/NqWFlq89lbS90PNQZ1sNHfNooeDec1DXEhPfrzcHMwpWAcMRUL9pv2N6bUHodh193r3krYbi02it4urFcWuVTuc8rtOPKbGwAAALkkq4H7ww8/1Lnnnht7fNNNN0mSrrnmGs2bNy9LrQKQE1xF0pDx1hEnFAxq4cKFmn3BeXKGGqXmQ2kchyV/XRqNMKWWw9ZxaEua7S9OHC1P9XD2bME4wzBU4nGqxOPUqAGpLSIZX+pe0xhMWtpeE1f2nk6pe8g00i51L3Y72vZE93Xcaq19WC/vYak7AABAX8lq4J4+fbpyZM02APnG4ZG8JVLJ0PTeFw5KLbVW+E4nrLcclsxI6r8n0GAdtTt68L06KXHv6nAVpz1PPb7UPVUtwbAON0VH0WPbrsXNRY+Wvm/bc1Bhu0c1aZS6N/hDavCHtKMmyRZ0ScSXurcG8QHFcXultxtFryhyqchll8Gy7gAAoI8whxtA/2J3SkUDrSMdkYg1Ot7ZyHlXYT2SxirloRZr/nonc9g7ZXMkD+Kdzl3v2Tx1j9OuoWV2DS3zdPqaYGsVwuxpcjgcsVL31nB+sDGQdB56X5S6u+y2tlH01lXduwjrFUWUugMAgJ4jcANAKmy2aEgtlzQm9feZpjUfvbvR82TBPZjaomaSrHnqjfs7Xfm9c5mdp96TUvdwxFRtc1A1jf5YqfuhdqXt8fPRaxrSWNU9nP6q7rFS9/gV3H2JJe7xYb3M65SdUncAACACNwBklmFI7mLrKK9K773Blrgwnovz1EuSlrzb3GUat3ePjNWHpOKBHV/TzTx1u82IlYGnqrXUvTWcx0bR24X1+MeZKnU3DKnc62wrZfe1lbQPaPe40udSZTGl7gAAFCoCNwDkKqdHcg49gnnq6QT1aFhXGutqBOqto3Z7wmW7pBMladezyd+XgXnqqZS6xzNNU42BcOLq7d2E9VRL3U1TOtQU1KGmYNql7h3mnXca1il1BwAgHxC4AaDQHNE89c4WlOtqpL3GKmlPVW/PU+8uvLvLrCkBcQzDULHboWK3owel7tFg3pB8Rfe2kvegGvyp/XfpSal7kcuuyuK48vb4kveEReOsOeuUugMA0PcI3AAAi83WFlKPYJ56qGG/PnrnDU06fowcgfYLzbUL7qHUyrQlHdk8dW95FwvIpTZPvSel7v5Q2FowLn67tfZhPbYtm7XyeyCc2mr4jYGwGmua0y51j4Vzn1N1B2xa8df1KnI75XHa5XPZ5XXZ5XHa5W094h+7En867Qal8AAAdIHADQA4Mu3mqZvBoHava5A5abbk7GZhtWBzalu0Jcxl78E89db39tI89e73U7dK292OnpW6J6ze3mVYt/ZRT7fUfbNaS91t+tv+NLeui2O3GfI6o4HcZYuFdE+ScN5ZeG99rc+V5L1Ou9wOG/utAwDyFoEbAJA9Tq91lA5L731ZnqfeLYe3kzL3roO74SqOlbpXVfas1L21rL3D9mtNbaPoqZa6p/K7WxeVyySP05YQ2hPCepLw7o2O1nf1vNdpl8dlk8/lkMdhk8Nu674hAACkicANAMg/RzxPPY191GP7qaczT71Zqm+W6nel174ezFO3eytU6S1Lu9R9f22T/rroDU2ZeqaCEUPNwbBagmE1B8NqDkTUFAglPI49H4heiz8PxL03GE5pxD0dLcGIWoIRHVIae9qnyWW3WcG+03Bul6/L8J44kt9+dN/jssllt1GCDwD9DIEbANB/JMxTT4NpSoGGFEN6FueppxLWPeVyeys0xFmsEZ6gThpeKqcr9bDeHdM05Q9F4sJ6WE3xgTwa0tvCe2KY7/S17R6nuq1bqgLhiALhiOpaMjdabzOUUFrfWRl9fHh32aUtuw3Vf7hTxR5Xh9d6XbbYSL7P5aAEHwByDIEbAIDuGIbkLrGO8lHpvTfVeertA3ugPo1fEjdPPUVOSZdIMj+xWVu1OTxWeX/Snx6rTD6Fn4bDK4/TI4/Dq/LW68Wtz/usn7YjL98OhqNBPcmIe0vcqHxzINTlKH3SoB+95g+ltoBdqiJmdLG7QDjNd9r1wtZ1Kb86vgS//ch7WzjvbpTeFr0p4EgYpW89pwQfAFJD4AYAIJOOZJ56OkE9trBcevPUDTMiBZusI43B+CNid8UF9a6CfufB3+nwyun0qLR98PfEP44GfLuz0z3cuxKJmGoJJQb5pkCykfeOYT4W4JOE+4Sgn6cl+E670enq9d0F/c7CffvPogQfQCEgcAMAkIvsTql4kHWkI8V56pHGg6rZvU2VpV7ZQn6r9D3Y0vYznPqe4GkLB6zDX5u53xHPsHUxMp8k2EfDvc3hlc/pkS/Ze10eydf+emnb4xRH8ZOV4MeP0jc0+/XeipU6dvxJCoTVoQQ/FubbB/0Ml+AHw6aC4ZDq+7AEv8N5u0X0Or7WlrC4ns/l6BDuPQ47JfgAMorADQBAIUlxnno4GNQ7Cxdq9uzZsiXbvi0SkUIt1hFsPrKfoZbEMB/72e61Zrql1ikyI1Kw0Tr6Soqj+IbTK4/DI4/Tq/Ik5fshm1MVzrWaXOGVw1Mc97w38QZAN6P4wXBbqG8JRNQUDCUvwU9Wpt/JKH3717YEc6UEPz1uh61DaO8s3CeE+bhye4fN1GeHDQ3cWiOf2yWXw1okz+Wwxc6drdfszLMH+hMCNwAA6Mhmk1w+6+gr4eCRh/ukwb6Tn3kwiu+QdLokbb6/6xd2M4rvdHrldHhU0l3Zvs8rlSZ7vkhyuBM/u90ofrIS/Pgg37qafVOHhfM6Cfp9VILvD0XkD0V0+IhL8O16ZP2HKb3SYTMSw7jdJrejLaA77R0De1twN+Sy2+OeM+JeY5cz+tid5LOS/h5uBAAZReAGAAC5we60DpX2ze/rzVH8VIO/2bujwDE5MIpvc3rlc3jkS3UufnGymwNJyvPjRvFNWSvKt8QF+dgWdl2M0scH/c6ejw/4vV2C314oYioUXawvl8TfCGgN6u5k4bx9eG9/I8BuJB/dj94IcNo7Ptfh93AjAAWCwA0AAPqnvh7FN01rFD/VEfhQs8L+Rn2+9hMdM3aU7JFAQY7ip8ywyXB45XZ65HZ4VdbdXPz4n96unvd1uB60udRiOtUcMhPCfXw4j43UB8JqaAlo/Ya/q+qoMQqbhrXNXCiiYPRnIBSJXQvEXQu2vxb9Gcls3u9Uvt0IaB/OO4b31tF/u+yGqe3bbdq6dLO8bkeHGwidTQHo8Hu4EYA0EbgBAAD6gmFIDpd1eMpSekskGNTnBxeqevps2ZPNte/2AxjF7wln9Cixu7vfDs/hVsTu1jbt0SijWnaX2xr9t7uiVRupnHsSrocMh0JyKmDa5ZddAdOhoBzymw4FwlIgHFYgZCYN9v5wRMEkwd4fvdb+ufZhv6ubBPl/I8Cm177Y2CttktpuBHQV3q3Qb48L7/Gj/3Y5HYbccVUDyaYRJJsC0P5GQKzigBsBOYfADQAAUKjyYBS/y4X0Ugn84UDmvk/YH60S6HoU3yZpjCQdfLNXfq0jeniSPWnYkwT37oJ93DVXqjcB2p+7FbZZNwKCciggu/XTbD3s8sshf8Quv2lXIGJXIGJ2G+yT3QiI3SBofS7+JkAookDYVCAUjj5nKpylOwGtNwKk3KsI6Cq8t4V+e/SakTTYJ7sREB/su7tJEPucfn4jgMANAACA3tGDUfwjFgmnVmof8uf+KH4qzHD0+zT3+a+2Rw93qm+w9eCGgNMpeVK9CWD9jNicCsmqAggZ1s+gHAqaDgUNh/ymXc0hacXH63TcSacoZHPLH7GrxbSrJWKXP2xTIGImhH5/u5L/hBsErc8lVBGY0edy5EZAxJrykEvsNqND6X77GwFOu6HaGptGTqjV5DEDs93kXkHgBgAAQP6y2SVXkXX0hSSj+MHmBi1/6w2dPfV0OYxIdH57sG2ee0/PI8G46+m8L3P7o6clErSOI10Avhs2Sa7o0ZXJkrSzkydTvSHgcEru1G4IRGxOhQ2HQoZ1Q6D1Z9CwbgYE5FDQbKsUaJ0+4G+9ERCxy2861BKxqyViU3PELn/ESKgaCHZyg6D1JkDbTQJrMcBAOJK1GwHhiKnmlG4E2FTbkuE/NH2IwA0AAACkKtkofnFQdb5NMkdMskZosy0SiYb1Xgr+KZ334LUhv6QsTQxvr7WNvcgWPXr1T4Rh635KgavrmwcRm0sRm0Nhw6mQYd0UCMuZUB0Qir8pEJ1C4DetKQOB1qkDrVUC0RsEzWG7WsJ2NUdsaonY5A+rrfw/NnXA7LSKIP5GgMtu6+I/Qn4hcAMAAACFxGaTbG5r7/RcFwn3yU2ASNCvL3Zs1Yghg2QzQz27eZALzLiFEHuo9UaAQ2lMD+jRL3IkD/7O5DcETJsV+nftPajB7rmShmSydX2GwA0AAAAgO2x2yea1tmXLoHAwqI8WLtTQ2bNl60kVgmlapfoZH/3vyWe0e00kR8qxIyHrSLE5hqxqgNGSQi01GWxY3yJwAwAAAEBXDCM6IuuU1EfrBfRU6zoDGb8J0NPPjrtmdjKf254DUzN6CYEbAAAAAApF/DoDuS4STgjiQX+j3nx9kc4dPjnbLes1BG4AAAAAQN+z2a3DGd113lWqZvegjE8x6EuFs/wbAAAAAAA5hMANAAAAAEAGELgBAAAAAMgAAjcAAAAAABlA4AYAAAAAIAMI3AAAAAAAZACBGwAAAACADCBwAwAAAACQAQRuAAAAAAAygMANAAAAAEAGELgBAAAAAMgAAjcAAAAAABlA4AYAAAAAIAMI3AAAAAAAZACBGwAAAACADCBwAwAAAACQAQRuAAAAAAAywJHtBhwJ0zQlSXV1dVluSdeCwaCamppUV1cnp9OZ7eagE/RTfqCfch99lB/op/xAP+UH+in30Uf5IV/6qTV/tubRruR14K6vr5ckVVVVZbklAAAAAID+pL6+XmVlZV2+xjBTieU5KhKJaNeuXSopKZFhGNluTqfq6upUVVWlHTt2qLS0NNvNQSfop/xAP+U++ig/0E/5gX7KD/RT7qOP8kO+9JNpmqqvr9fw4cNls3U9SzuvR7htNptGjhyZ7WakrLS0NKf/4MBCP+UH+in30Uf5gX7KD/RTfqCfch99lB/yoZ+6G9luxaJpAAAAAABkAIEbAAAAAIAMsN9+++23Z7sR/YHdbtf06dPlcOR1FX/Bo5/yA/2U++ij/EA/5Qf6KT/QT7mPPsoPhdZPeb1oGgAAAAAAuYqScgAAAAAAMoDADQAAAABABhC4AQAAAADIAAI3AAAAAAAZQODuJQ899JCOOuooeTwenX766frggw86fe1jjz2ms88+WxUVFaqoqNCMGTO6fD16Tzr9tGDBAp166qkqLy9XUVGRJk6cqD/84Q992Nr+K51+ivfss8/KMAxdfvnlGW4h0umjefPmyTCMhMPj8fRha/uvdP8uHT58WNdff72GDRsmt9utY445RgsXLuyj1vZf6fTT9OnTO/x9MgxDF198cR+2uP9J9+/S/fffr2OPPVZer1dVVVW68cYb1dLS0ket7b/S6adgMKg777xT48aNk8fj0cknn6xXX321D1vb/yxbtkyXXHKJhg8fLsMw9OKLL3b7nqVLl2rSpElyu92qrq7WvHnzMt/Q3mbiiD377LOmy+Uyn3zySXPt2rXmP/7jP5rl5eXm3r17k75+zpw55kMPPWSuWrXKXL9+vXnttdeaZWVl5s6dO/u45f1Luv305ptvmgsWLDDXrVtnbty40bz//vtNu91uvvrqq33c8v4l3X5qtWXLFnPEiBHm2WefbV522WV91Nr+Kd0+euqpp8zS0lJz9+7dsWPPnj193Or+J91+8vv95qmnnmrOnj3bXL58ubllyxZz6dKl5urVq/u45f1Luv108ODBhL9Ln376qWm3282nnnqqbxvej6TbR88884zpdrvNZ555xtyyZYv52muvmcOGDTNvvPHGPm55/5JuP/3kJz8xhw8fbr788svmpk2bzIcfftj0eDzmRx991Mct7z8WLlxo3nbbbeaCBQtMSeYLL7zQ5es3b95s+nw+86abbjLXrVtn/va3v83Lf4sTuHvBlClTzOuvvz72OBwOm8OHDzfvvvvulN4fCoXMkpIS8+mnn85UE2EeeT+Zpmmecsop5n/8x39konmI6kk/hUIh84wzzjAff/xx85prriFwZ1i6ffTUU0+ZZWVlfdU8RKXbT4888og5duxYMxAI9FUTYR75/zf9+te/NktKSsyGhoZMNbHfS7ePrr/+evO8885LuHbTTTeZZ555Zkbb2d+l20/Dhg0zH3zwwYRrV1xxhXnVVVdltJ2wpBK4f/KTn5jjx49PuPbVr37VnDVrViab1usoKT9CgUBAK1eu1IwZM2LXbDabZsyYoffeey+lz2hqalIwGFRlZWWmmtnvHWk/maapJUuWaMOGDTrnnHMy2dR+raf9dOedd2rw4MH6zne+0xfN7Nd62kcNDQ0aPXq0qqqqdNlll2nt2rV90dx+qyf99NJLL2nq1Km6/vrrNWTIEJ144om66667FA6H+6rZ/U5v/BviiSee0Ne+9jUVFRVlqpn9Wk/66IwzztDKlStj5cybN2/WwoULNXv27D5pc3/Uk37y+/0dpjd5vV4tX748o21F6t57772EPpWkWbNmpfy/j7nCke0G5LsDBw4oHA5ryJAhCdeHDBmizz77LKXPuPnmmzV8+PAOf6DQe3raT7W1tRoxYoT8fr/sdrsefvhhzZw5M9PN7bd60k/Lly/XE088odWrV/dFE/u9nvTRscceqyeffFITJkxQbW2tfvWrX+mMM87Q2rVrNXLkyL5odr/Tk37avHmz3njjDV111VVauHChNm7cqOuuu07BYFA/+9nP+qLZ/c6R/hvigw8+0KeffqonnngiU03s93rSR3PmzNGBAwd01llnyTRNhUIhff/739e///u/90WT+6We9NOsWbN033336ZxzztG4ceO0ZMkSLViwgJuMOWTPnj1J+7Surk7Nzc3yer1Zall6GOHOsnvuuUfPPvusXnjhBRYRykElJSVavXq1VqxYoblz5+qmm27S0qVLs90sRNXX1+vqq6/WY489poEDB2a7OejE1KlT9c1vflMTJ07UtGnTtGDBAg0aNEi/+93vst00xIlEIho8eLB+//vfa/LkyfrqV7+q2267TY8++mi2m4ZOPPHEEzrppJM0ZcqUbDcFcZYuXaq77rpLDz/8sD766CMtWLBAL7/8sn7+859nu2mI88ADD+joo4/WcccdJ5fLpRtuuEHf+ta3ZLMRj9C7GOE+QgMHDpTdbtfevXsTru/du1dDhw7t8r2/+tWvdM899+j111/XhAkTMtnMfq+n/WSz2VRdXS1JmjhxotavX6+7775b06dPz2Rz+610+2nTpk3aunWrLrnkkti1SCQiSXI4HNqwYYPGjRuX2Ub3M0fyv3mtnE6nTjnlFG3cuDETTYR61k/Dhg2T0+mU3W6PXTv++OO1Z88eBQIBuVyujLa5PzqSv0+NjY169tlndeedd2ayif1eT/ropz/9qa6++mp997vflSSddNJJamxs1Pe+9z3ddtttBLoM6Ek/DRo0SC+++KJaWlp08OBBDR8+XLfccovGjh3bF01GCoYOHZq0T0tLS/NmdFtihPuIuVwuTZ48WUuWLIldi0QiWrJkiaZOndrp++699179/Oc/16uvvqpTTz21L5rar/W0n9qLRCLy+/2ZaCKUfj8dd9xxWrNmjVavXh07Lr30Up177rlavXq1qqqq+rL5/UJv/F0Kh8Nas2aNhg0blqlm9ns96aczzzxTG+nXgkMAAArnSURBVDdujN20kqTPP/9cw4YNI2xnyJH8ffrzn/8sv9+vb3zjG5luZr/Wkz5qamrqEKpbb2SZppm5xvZjR/J3yePxaMSIEQqFQnr++ed12WWXZbq5SNHUqVMT+lSSFi9enNa/3XNClhdtKwjPPvus6Xa7zXnz5pnr1q0zv/e975nl5eWxbW+uvvpq85Zbbom9/p577jFdLpf5l7/8JWFrj/r6+mx9hX4h3X666667zEWLFpmbNm0y161bZ/7qV78yHQ6H+dhjj2XrK/QL6fZTe6xSnnnp9tEdd9xhvvbaa+amTZvMlStXml/72tdMj8djrl27NltfoV9It5+2b99ulpSUmDfccIO5YcMG869//as5ePBg8xe/+EW2vkK/0NP/zTvrrLPMr371q33d3H4p3T762c9+ZpaUlJh//OMfzc2bN5uLFi0yx40bZ37lK1/J1lfoF9Ltp/fff998/vnnzU2bNpnLli0zzzvvPHPMmDHmoUOHsvUVCl59fb25atUqc9WqVaYk87777jNXrVplbtu2zTRN07zlllvMq6++Ovb61m3B/u3f/s1cv369+dBDD7EtWH/229/+1hw1apTpcrnMKVOmmO+//37suWnTppnXXHNN7PHo0aNNSR2On/3sZ33f8H4mnX667bbbzOrqatPj8ZgVFRXm1KlTzWeffTYLre5/0umn9gjcfSOdPvrRj34Ue+2QIUPM2bNns89pH0n379K7775rnn766abb7TbHjh1rzp071wyFQn3c6v4n3X767LPPTEnmokWL+ril/Vc6fRQMBs3bb7/dHDdunOnxeMyqqirzuuuuI8j1gXT6aenSpebxxx9vut1uc8CAAebVV19tfvHFF1lodf/x5ptvJs1Arf1yzTXXmNOmTevwnokTJ5oul8scO3as+dRTT/V5u4+UYZrUtgAAAAAA0NuYww0AAAAAQAYQuAEAAAAAyAACNwAAAAAAGUDgBgAAAAAgAwjcAAAAAABkAIEbAAAAAIAMIHADAAAAAJABBG4AAAAAADKAwA0AQB4IBAKqrq7Wu+++m+2mdHDttdfq8ssv79XPfPTRR3XJJZf06mcCANDXCNwAAGTA/v379YMf/ECjRo2S2+3W0KFDNWvWLL3zzjs9+rxHH31UY8aM0RlnnBG7ZhhGh+Oss87qra/QwdatW2UYhlavXp1w/YEHHtC8efN69Xd9+9vf1kcffaS33367Vz8XAIC+5Mh2AwAAKERXXnmlAoGAnn76aY0dO1Z79+7VkiVLdPDgwbQ/yzRNPfjgg7rzzjs7PPfUU0/pwgsvjD12uVxJPyMYDMrpdKb9u1NRVlbW65/pcrk0Z84c/eY3v9HZZ5/d658PAEBfYIQbAIBedvjwYb399tv6z//8T5177rkaPXq0pkyZoltvvVWXXnqpfvzjH+tLX/pS7PX333+/DMPQq6++GrtWXV2txx9/XJK0cuVKbdq0SRdffHGH31VeXq6hQ4fGjsrKythI9J/+9CdNmzZNHo9HzzzzjA4ePKivf/3rGjFihHw+n0466ST98Y9/TPi8SCSie++9V9XV1XK73Ro1apTmzp0rSRozZowk6ZRTTpFhGJo+fbqkjiXlfr9f//Iv/6LBgwfL4/HorLPO0ooVK2LPL126VIZhaMmSJTr11FPl8/l0xhlnaMOGDQltueSSS/TSSy+pubm5J90AAEDWEbgBAOhlxcXFKi4u1osvvii/39/h+WnTpmn58uUKh8OSpLfeeksDBw7U0qVLJUlffPGFNm3aFAu0b7/9to455hiVlJSk1Y5bbrlFP/zhD7V+/XrNmjVLLS0tmjx5sl5++WV9+umn+t73vqerr75aH3zwQew9t956q+655x799Kc/1bp16zR//nwNGTJEkmKve/3117V7924tWLAg6e/9yU9+oueff15PP/20PvroI1VXV2vWrFmqqalJeN1tt92m//qv/9KHH34oh8Ohb3/72wnPn3rqqQqFQvrb3/6W1vcGACBXELgBAOhlDodD8+bN09NPP63y8nKdeeaZ+vd//3d98sknkqSzzz5b9fX1WrVqlUzT1LJly/Sv//qvscC9dOlSjRgxQtXV1ZKkbdu2afjw4Ul/19e//vVYwG8N+a1+9KMf6YorrtCYMWM0bNgwjRgxQj/+8Y81ceJEjR07Vv/8z/+sCy+8UM8995wkqb6+Xg888IDuvfdeXXPNNRo3bpzOOussffe735UkDRo0SJI0YMCA2Gh6e42NjXrkkUf0y1/+UhdddJFOOOEEPfbYY/J6vXriiScSXjt37lxNmzZNJ5xwgm655Ra9++67amlpiT3v8/lUVlambdu29aQbAADIOgI3AAAZcOWVV2rXrl166aWXdOGFF2rp0qWaNGmS5s2bp/Lycp188slaunSp1qxZI9f/3969vML3x3Ecf6kvzZgpkWQWNuMSykiJhTBossSeUhqXP4ApuRQLt4X4E0QWthYjCbmkLEgxmhrKgkYhuRN+C33P1/S1wffwU89HnTqdzznn8/4sX33O53NiYtTY2KiNjQ1dXl5qcXFRpaWlxrtubm5ksVje7Gd4eFibm5vG4fF4jLb8/PyIex8fH9Xb26ucnBwlJCTIbrdrZmZGBwcHkqRAIKC7uztVVFR8eNyhUEgPDw8qKioyrkVHR6ugoECBQCDiXpfLZZw7HA5J0vHxccQ9VqtV19fXH64HAIDvROAGAMAkFotFHo9HnZ2dWl1dVX19vbq7uyVJbrdbCwsLRrhOSEhQVlaWlpeX/wrciYmJOjs7e7OP5ORkpaWlGYfNZjPaXp9L0tDQkEZGRuTz+TQ/P6/NzU1VVlbq/v5e0ku4/UqvN3GLioqS9LKG/LXT01NjZh0AgJ+GwA0AwBfJzs7W1dWVpD/ruOfm5oy12m63W5OTkwoGg8Y16WWTst3dXT0/P3+q/5WVFVVVVam2tla5ublyOp0KBoNGe3p6uqxWq+bm5t58/vcO6L/Xnr8lNTVVMTExEb8/e3h40Pr6urKzs99VbygU0u3trfLy8t71HAAA/xcEbgAA/rGTkxOVl5drfHxcW1tb2t/f19TUlAYHB1VVVSVJKikp0cXFhaanpyMC98TEhBwOhzIyMoz3lZWV6fLyUtvb25+qKz09XbOzs1pdXVUgEFBTU5PC4bDRbrFY5PP51NbWprGxMYVCIa2trRlrr5OSkmS1WuX3+xUOh3V+fv5XHzabTS0tLWptbZXf79fOzo68Xq+ur6/V0NDwrnqXlpbkdDqVmpr6qXEDAPBd+A83AAD/mN1uV2FhoYaHh401zSkpKfJ6vWpvb5ckxcfHKycnR+FwWJmZmZJeQvjT01PE5+TSyyZlNTU1mpiYUF9f34fr6ujo0N7eniorKxUbG6vGxkZVV1dHBOfOzk79+vVLXV1dOjw8lMPhUHNzs6SXzeBGR0fV09Ojrq4uFRcXGxu9vdbf36+npyfV1dXp4uJC+fn5mpmZUXx8/LvqnZyclNfr/fB4AQD4blHPn/0+DQAAmG5ra0sej0ehUEh2u/27yzHd9va2ysvLFQwGFRcX993lAADwIXxSDgDAD+ByuTQwMKD9/f3vLuVLHB0daWxsjLANAPjRmOEGAAAAAMAEzHADAAAAAGACAjcAAAAAACYgcAMAAAAAYAICNwAAAAAAJiBwAwAAAABgAgI3AAAAAAAmIHADAAAAAGACAjcAAAAAACYgcAMAAAAAYIL/AA/qI1i3rDaJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrbS3AknK3iS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}